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Record W6961394624 · doi:10.15139/s3/ltfcth

Sexual Decision Making, 2013

2020· dataset· en· W6961394624 on OpenAlexaffabout

Bibliographic record

VenueUNC Dataverse · 2020
Typedataset
Languageen
Field
Topic
Canadian institutionsUniversity of TorontoYork University
Fundersnot available
KeywordsEthnic groupComputer-assisted web interviewingYoung adultLatin AmericansVariety (cybernetics)MEDLINEPublic healthSexual behavior

Abstract

fetched live from OpenAlex

Participants were recruited through online postings and classroom visits at a small Canadian university and through online postings on the websites Kijiji and Craigslist in the Greater Toronto Area. To be eligible to participate, both members of the couple had to agree to take part in the study and be over the age of 18. Eligible couples also had to see their partner several times a week and be sexually active. Interested participants who met the eligibility criteria emailed the researchers for more information about the study. After couples agreed to participate, each partner was e-mailed a unique link allowing them to access the online surveys. A total of 101 couples (95 mixed-sex, 6 same-sex) ranging in age from 18 to 53 years (M 26 years, SD 7 years) participated in the study. Nearly half the participants were cohabiting (29%), married (17%), or engaged (3%); the remaining participants were in a committed relationship, but not living together. Participants reported being involved their current relationship between 6 months and 22 years (M 4.45 years, SD 3.76 years) and identified as a diverse variety of ethnic backgrounds; 67% were White, 8% were Asian, 7% were Black, 4% were South Asian, 4% were Latin American, 4% were South East Asian, 1% were Arab/West Asian, and 5% identified as multiethnic or “other.” On the first day of the study, participants completed a 30-min background survey. Then, each day for 21 consecutive days, participants completed a 5- to 10-min daily survey. Participants were asked to begin the study on the same day as their romantic partner and to refrain from discussing their responses with their partner until the completion of the study. Each participant was paid up to $40 CAD (in gift cards) for completing the background and daily surveys; payment was prorated based on the number of daily diaries completed. Participants completed an average of 18 (of 21) daily surveys (M 18.48, SD 5.06, range 1–21). Participants also completed a 3 month follow-up survey.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.081
Threshold uncertainty score0.271

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0810.008

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.030
GPT teacher head0.302
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreDataset

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2020
Admission routes2
Has abstractyes

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Same venueUNC DataverseFrench-language works237,207